SOTAVerified

Common Sense Reasoning

Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.

Papers

Showing 251–275 of 939 papers

TitleStatusHype
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering—0
Exploiting Proximity-Aware Tasks for Embodied Social Navigation—0
Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation—0
Distributional semantics for ontology verification—0
Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection—0
Cooperating with Machines—0
Ambiguss, a game for building a Sense Annotated Corpus for French—0
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules—0
Stereotype Detection in LLMs: A Multiclass, Explainable, and Benchmark-Driven Approach—0
AbductionRules: Training Transformers to Explain Unexpected Inputs—0
Conversational AI : Open Domain Question Answering and Commonsense Reasoning—0
Converging Measures and an Emergent Model: A Meta-Analysis of Human-Automation Trust Questionnaires—0
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos—0
A Tool for Extracting Conversational Implicatures—0
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models—0
A mathematical theory of super-resolution and two-point resolution—0
Affordance Extraction and Inference based on Semantic Role Labeling—0
EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries—0
Context-based Natural Language Processing for GIS-based Vague Region Visualization—0
Content selection as semantic-based ontology exploration—0
ATLAS: Learning to Optimally Memorize the Context at Test Time—0
Bridging Visual Perception with Contextual Semantics for Understanding Robot Manipulation Tasks—0
Constructing a Dictionary Describing Feature Changes of Arguments in Event Sentences—0
A Theory of Human-Like Few-Shot Learning—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy96.1—Unverified
2Unicorn 11B (fine-tuned)Accuracy91.3—Unverified
3CompassMTL 567M with TailorAccuracy90.5—Unverified
4CompassMTL 567MAccuracy89.6—Unverified
5UnifiedQA 11B (fine-tuned)Accuracy89.4—Unverified
6Claude 3 Opus (5-shot)Accuracy88.5—Unverified
7GPT-4 (5-shot)Accuracy87.5—Unverified
8ExDeBERTa 567MAccuracy87—Unverified
9LLaMA-2 13B + MixLoRAAccuracy86.3—Unverified
10LLaMA3 8B+MoSLoRAAccuracy85.8—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4 (few-shot, k=25)Accuracy96.4—Unverified
2PaLM 2 (few-shot, CoT, SC)Accuracy95.1—Unverified
3Shivaay (4B, few-shot, k=8)Accuracy91.04—Unverified
4StupidLLMAccuracy91.03—Unverified
5Claude 2 (few-shot, k=5)Accuracy91—Unverified
6Claude 1.3 (few-shot, k=5)Accuracy90—Unverified
7PaLM 540B (Self Improvement, Self Consistency)Accuracy89.8—Unverified
8PaLM 540B (Self Consistency)Accuracy88.7—Unverified
9PaLM 540B (Self Improvement, CoT Prompting)Accuracy88.3—Unverified
10PaLM 540B (Self Improvement, Standard-Prompting)Accuracy87.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy95.2—Unverified
2LLaMA 3 8B+MoSLoRA (fine-tuned)Accuracy90.5—Unverified
3PaLM 2-L (1-shot)Accuracy89.7—Unverified
4PaLM 2-M (1-shot)Accuracy88—Unverified
5LLaMA-3 8B + MixLoRAAccuracy86.5—Unverified
6Camelidae-8×34BAccuracy86.2—Unverified
7PaLM 2-S (1-shot)Accuracy85.6—Unverified
8LLaMA 65B + CFG (0-shot)Accuracy84.2—Unverified
9GAL 120B (0-shot)Accuracy83.8—Unverified
10LLaMA-2 13B + MixLoRAAccuracy83.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Turing NLR v5 XXL 5.4B (fine-tuned)EM95.9—Unverified
2ST-MoE-32B 269B (fine-tuned)EM95.1—Unverified
3T5-11BF194.1—Unverified
4DeBERTa-1.5BEM94.1—Unverified
5PaLM 540B (finetuned)EM94—Unverified
6Vega v2 6B (fine-tuned)EM93.9—Unverified
7PaLM 2-L (one-shot)F193.8—Unverified
8T5-XXL 11B (fine-tuned)EM93.4—Unverified
9PaLM 2-M (one-shot)F192.4—Unverified
10PaLM 2-S (one-shot)F192.1—Unverified